Advanced curriculum
Deep-dive lectures
Three full undergraduate lectures that broaden the assessment knowledge beyond the FastAPI track — architectures, loss functions, and the math behind modern damage-assessment models.
L1Lecture
Vision Transformers (ViT)
Patches, self-attention, multi-head MSA, encoder block, variants (DeiT, Swin, BEiT), and how residuals defeat vanishing gradient.
- 12 sections
- Q/K/V math
- Architecture diagrams
L2Lecture
Focal Loss, Dice Loss & Class Weights
Why cross-entropy fails on imbalanced disaster data, the (1−pₜ)^γ modulating factor, region-overlap losses, and combined Focal+Dice training.
- Class imbalance
- Worked numerics
- PyTorch code
L3Lecture
Siamese Neural Networks
Shared encoders, difference blocks, skip connections, attention gates, deep supervision, and Siamese variants for pre/post change detection.
- Twin networks
- xBD pipeline
- Deep supervision